Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments

Contaminated sediments and other sites present a difficult challenge for environmental decisionmakers. They are typically slow to recover or attenuate naturally, may involve multiple regulatory agencies and stakeholder groups, and engender multiple toxicological and ecotoxicological risks. While environmental decision-making strategies over the last several decades have evolved into increasingly more sophisticated, information-intensive, and complex approaches, there remains considerable dissatisfaction among business, industry, and the public with existing management strategies. Consequently, contaminated sediments and materials are the subject of intense technology development, such as beneficial reuse or in situ treatment. However, current decision analysis approaches, such as comparative risk assessment, benefit-cost analysis, and life cycle assessment, do not offer a comprehensive approach for incorporating the varied types of information and multiple stakeholder and public views that must typically be brought to bear when new technologies are under consideration. Alternatively, multicriteria decision analysis (MCDA) offers a scientifically sound decision framework for management of contaminated materials or sites where stakeholder participation is of crucial concern and criteria such as economics, environmental impacts, safety, and risk cannot be easily condensed into simple monetary expressions. This article brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in MCDA methods applicable to the assessment of contaminated sediment management technologies. Additionally, it tests an MCDA approach for coupling expert judgment and stakeholder values in a hypothetical contaminated sediments management case study wherein MCDA is used as a tool for testing stakeholder responses to and improving expert assessment of innovative contaminated sediments technologies.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

Decision Analysis and Risk Management Decision Making: Issues and Methods

This paper provides an overview of decision analysis and its use in risk management decision making. The paper discusses the distinctive characteristics of decision analysis and compares these characteristics with those of its principal alternative—cost–benefit analysis. The paper also discusses each of the steps in a decision analysis and the strengths and limitations of the method.     

Ethics, Decision Analysis, and Public Risk

In any analysis of a decision problem involving public risks, ethical implications are introduced. In some cases, these ethical implications may be introduced simply because an analysis is being done. Additional ethical implications may be inherently part of the methodology being utilized or introduced into the specific analysis of the decision problem. In this paper, we investigate where and how ethical implications enter when using the methodology of decision analysis to examine problems involving public risks. We conclude that the methodology of decision analysis is sufficiently robust to allow for numerous different ethical viewpoints to be accounted for in any specific analysis. Stated alternatively, decision analyses of public risks can be conducted in a manner consistent with utilitarianism, deontological theories, libertarianism, egalitarianism, and so forth. However, any specific analysis has embedded within it numerous ethical implications. This suggests that the careful ethical scrutiny of analyses involving the methodology of decision analysis should be placed on the specific application and not on the methodology per se or on the fact that an analysis is undertaken.     

Cognitive and Motivational Biases in Decision and Risk Analysis

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.     

Risk Ranking of Antimicrobial‐Resistant Hazards Found in Meat in Switzerland

Human exposure to bacteria resistant to antimicrobials and transfer of related genes is a complex issue and occurs, among other pathways, via meat consumption. In a context of limited resources, the prioritization of risk management activities is essential. Since the antimicrobial resistance (AMR) situation differs substantially between countries, prioritization should be country specific. The objective of this study was to develop a systematic and transparent framework to rank combinations of bacteria species resistant to selected antimicrobial classes found in meat, based on the risk they represent for public health in Switzerland. A risk assessment model from slaughter to consumption was developed following the Codex Alimentarius guidelines for risk analysis of foodborne AMR. Using data from the Swiss AMR monitoring program, 208 combinations of animal species/bacteria/antimicrobial classes were identified as relevant hazards. Exposure assessment and hazard characterization scores were developed and combined using multicriteria decision analysis. The effect of changing weights of scores was explored with sensitivity analysis. Attributing equal weights to each score, poultry‐associated combinations represented the highest risk. In particular, contamination with extended‐spectrum β‐lactamase/plasmidic AmpC‐producing Escherichia coli in poultry meat ranked high for both exposure and hazard characterization. Tetracycline‐ or macrolide‐resistant Enterococcus spp., as well as fluoroquinolone‐ or macrolide‐resistant Campylobacter jejuni , ranked among combinations with the highest risk. This study provides a basis for prioritizing future activities to mitigate the risk associated with foodborne AMR in Switzerland. A user‐friendly version of the model was provided to risk managers; it can easily be adjusted to the constantly evolving knowledge on AMR.     

Decision Making Under Uncertainty—An Example for Seismic Risk Management

Decision-making techniques are used to select the "best" alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision-making process. The major thrust of this article is to extend the framework proposed by Yager^{( 1 )} for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step-by-step formulation of the proposed approach is illustrated using a hypothetical example and a three-story reinforced concrete building.     

A Decision Framework for Managing Risk to Airports from Terrorist Attack

This article presents an asset‐level security risk management framework to assist stakeholders of critical assets with allocating limited budgets for enhancing their safety and security against terrorist attack. The proposed framework models the security system of an asset, considers various threat scenarios, and models the sequential decision framework of attackers during the attack. Its novel contributions are the introduction of the notion of partial neutralization of attackers by defenders, estimation of total loss from successful, partially successful, and unsuccessful actions of attackers at various stages of an attack, and inclusion of the effects of these losses on the choices made by terrorists at various stages of the attack. The application of the proposed method is demonstrated in an example dealing with security risk management of a U.S. commercial airport, in which a set of plausible threat scenarios and risk mitigation options are considered. It is found that a combination of providing blast‐resistant cargo containers and a video surveillance system on the airport perimeter fence is the best option based on minimum expected life‐cycle cost considering a 10‐year service period.     

A Social Decision Analysis of the Earthquake Safety Problem: The Case of Existing Los Angeles Buildings

In this paper we propose a framework for conducting a decision analysis for a societal problem such as earthquake safety. The application deals with the formulation and evaluation of alternative policies for the seismic safety problem faced by the city of Los Angeles with regard to its old masonry buildings. A social decision analysis compares the costs and benefits of the alternative policies from the viewpoints of the impacted constituents. The emphasis is on identifying acceptable policy that considers the interests of the impacted constituents and provides incentives for their cooperation. Alternatives ranging from strict regulation to free market are examined. In order to evaluate the trade-offs between additional cost and savings in lives, a direct willingness-to-pay and an economic approach, based on property value differential, are used. Recommendations range from strict regulation for the residential and critical buildings (schools, hospitals, fire stations, etc.) to simply informing the occupants (in the case of commercial and industrial buildings) of the risks involved.     

Decision Making Under Risk: A Comparison of Bayesian and Fuzzy Set Methods

A classical decision problem is considered where a decision maker is to choose one of a number of actions each offering different consequences. The outcome from a choice of action is uncertain because it depends on the existing state of Nature. Also, the outcome, once an action and state of Nature are specified, may be a vector or a random vector. The decision maker employs both Bayesian methods and fuzzy set techniques to handle the uncertainties. The decision maker is also allowed to use multiple, possibly conflicting, goals in order to determine his best strategy. The Bayesian method produces a set of undominated strategies to choose from, whereas the fuzzy set technique usually produces a unique optimal strategy.     

Uses and Misuses of Multicriteria Decision Analysis (MCDA) in Environmental Decision Making

We focus on a class of multicriteria methods that are commonly used in environmental decision making—those that employ the weighted linear average algorithm (and this includes the popular analytic hierarchy process (AHP)). While we do not doubt the potential benefits of using formal decision methods of this type, we draw attention to the consequences of not using them well. In particular, we highlight a property of these methods that should not be overlooked when they are applied in environmental and wider decision-making contexts: the final decision or ranking of options is dependent on the choice of performance scoring scales for the criteria when the criteria weights are held constant. We compare this "sensitivity" to a well-known criticism of the AHP, and we go on to describe the more general lesson when it comes to using weighted linear average methods—a lesson concerning the relationship between criteria weights and performance scoring scales.     

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.     

Intergenerational Risk Decision Making: A Practical Example

There is no such thing as intergenerational decision making, at least not yet. In fact, there is no such thing as intragenerational decision making in the context of maximizing overall social good given resource limitations, there are just decisions being made in an ad hoc fashion. Even if one assumes that there is such a thing as intragenerational decision making, no uniform standard or guidance exists to make societal decisions for the common good. Risks to society are judged unevenly within the same agency and across agencies. Decisions are made in isolation and not weighed in the societal context of what is intra or intergenerationally important. The National Academy of Public Administration (NAPA) has set forth a framework for intergenerational decision making that provides a consistent and fair basis for making tough decisions in order to address difficult issues such as the long-term disposal of nuclear wastes. NAPA recognizes that there is an intergenerational obligation that must encompass broader questions than the narrow issue of waste disposal since resources are finite and needs are great. The fundamental principles are based on sustainability with the overarching objective that "no generation should needlessly, now or in the future, deprive its successors of the opportunity to enjoy a quality of life equivalent to its own." Coupled with this objective are four supporting principles of trusteeship, sustainability, chain of obligation, and precaution. The NAPA process also recognizes that no decision can be final and that a "rolling future" view is better than making decisions for "all time." It attempts to balance the needs of the present with those of the future in an open and transparent process that is aimed at producing a decision, not just endless analysis. The U.S. Congress and president should develop a rational standard by which to judge laws that involve intra and intergenerational issues relative to the overall societal good. Present regulations need to be evaluated relative to a uniform level of risk and benefit to assess where the limited money available can do the most good for both the present and future generations in the context of NAPA sustainability principles. It is hoped that decision makers will take a serious look at this process since it can work to resolve stakeholder stalemate.     

库存问题的子值结点决策影响图方法

本文运用子值结点决策影响图方法,表征求解带有预测和相关需求的库存问题,并将其与动态规划方法作了比较。     

Decision Making for Risk Management: A Comparison of Graphical Methods for Presenting Quantitative Uncertainty

Previous research has shown that people err when making decisions aided by probability information. Surprisingly, there has been little exploration into the accuracy of decisions made based on many commonly used probabilistic display methods. Two experiments examined the ability of a comprehensive set of such methods to effectively communicate critical information to a decision maker and influence confidence in decision making. The second experiment investigated the performance of these methods under time pressure, a situational factor known to exacerbate judgmental errors. Ten commonly used graphical display methods were randomly assigned to participants. Across eight scenarios in which a probabilistic outcome was described, participants were asked questions regarding graph interpretation (e.g., mean) and made behavioral choices (i.e., act; do not act) based on the provided information indicated that decision‐maker accuracy differed by graphical method; error bars and boxplots led to greatest mean estimation and behavioral choice accuracy whereas complementary cumulative probability distribution functions were associated with the highest probability estimation accuracy. Under time pressure, participant performance decreased when making behavioral choices.     

The Role of Society in Engineering Risk Analysis: A Capabilities-Based Approach

This article proposes a new conceptual framework in engineering risk analysis to account for the net impact of hazards on individuals in a society. It analyzes four limitations of prevailing approaches to risk analysis and suggests a way to overcome them. These limitations are a result of how societal impacts are characteristically accounted for and valued. Prevailing approaches typically focus too narrowly on the consequences of natural or man-made hazards, not accounting for the broader societal impacts of such hazards. Such approaches lack a uniform and consistent metric for accounting for the impact of the nonquantifiable consequences (like psychological trauma or societal impacts) and rely upon implicit and potentially inaccurate value judgments when evaluating risks. To overcome these limitations, we propose an alternative, Capabilities-Based Approach to the treatment of society in risk analysis. A similar approach is currently used by the United Nations to quantitatively measure the degree of development in countries around the world. In a Capabilities-Based Approach, the potential benefits and losses due to a hazard are measured and compared in a uniform way by using individual capabilities (functionings individuals are able, still able, or unable to achieve) as a metric. This Capabilities-Based Approach provides a foundation for identifying and quantifying the broader, complex societal consequences of hazards and is based on explicit, value judgments. The Capabilities-Based Approach can accommodate different methods or techniques for risk determination and for risk evaluation and can be used in assessing risk in diverse types of hazards (natural or man-made) and different magnitudes that range from minor to catastrophic. In addition, implementing a Capabilities-Based Approach contributes to the development of a single standard for public policy decision making, since a Capabilities-Based Approach is already in use in development economics and policy.     

Does a Reactor Need a Safety Backfit? Case Study on Communicating Decision and Risk Analysis Information to Managers

An approach to communicating decision and risk analysis findings to managers is illustrated in a real case context. This article consists essentially of a report prepared for senior managers of the Nuclear Regulatory Commission to help them make a reactor safety decision. It illustrates the communication of decision analysis findings relating to technical risks, costs, and benefits in support of a major risk management decision: whether or not to require a safety backfit. Its focus is on the needs of decision makers, and it introduces some novel communication devices.     

电视购物消费者知觉风险与购买决策行为分析

电视购物作为一种无店铺营销的商业零售模式,在国内的发展中一直深受信任危机的困扰。本研究引入心理学知觉风险的概念,通过调查目前消费者对电视购物的风险知觉及其购买行为,来了解目前国内电视购物的消费者对电视购物有怎样的风险知觉,消费者对电视购物的风险知觉对其购买决策有怎样的影响,以及消费者特征、消费者对电视购物的风险知觉和其购买决策行为之间的关系和影响作用。     

A review of representation issues and modeling challenges with influence diagrams

Since their introduction in the mid 1970s, influence diagrams have become a de facto standard for representing Bayesian decision problems. The need to represent complex problems has led to extensions of the influence diagram methodology designed to increase the ability to represent complex problems. In this paper, we review the representation issues and modeling challenges associated with influence diagrams. In particular, we look at the representation of asymmetric decision problems including conditional distribution trees, sequential decision diagrams, and sequential valuation networks. We also examine the issue of representing the sequence of decision and chance variables, and how it is done in unconstrained influence diagrams, sequential valuation networks, and sequential influence diagrams. We also discuss the use of continuous chance and decision variables, including continuous conditionally deterministic variables. Finally, we discuss some of the modeling challenges faced in representing decision problems in practice and some software that is currently available.